Construction industry productivity: Examining the effects of omitted variable bias in the census construction price index models

A fundamental challenge to the US construction industry is an establishment of an accurate measure of the industry's productivity. Macroeconomic studies have shown a decline in construction productivity (Stokes 1981, BRT 1983, Allen 1985, and Teicholtz 2000), while micro studies have shown an increase (Allmon et al. 2000 and Goodrum et al. 2002). One theory to explain these opposing results might be that current measurements of construction inflation do not adequately consider the change in quality and amenities provided in today's structures as compared to when the measurements were first established. An index that is considered to be one of the industry's major measurements of construction inflation is the U.S. Census Bureau New One-Family Houses under Construction Price Index. This dissertation examines the current hedonic regression model used by the Census Bureau to estimate this price index, which is used to help measure a significant proportion of the industry's real output. This study compares the current hedonic model with a proposed model, which will include quality variables which are not part of Census' current hedonic model. Previous research suggests that omitted quality variables in the Census' price index leads to an omitted variable bias which overestimates construction inflation, leading to both an underestimate of construction industry output and productivity. However, previous research has not formalized necessary changes to the Census index to avoid this bias, nor has the bias been actually measured in order to quantify its direction or magnitude. This study helps to determine the magnitude of the bias, as well as provide a direction for change if needed.;KEYWORDS: Real Construction Output, Construction Inflation Indexes, Census Indexes, Construction Quality Measurement, Construction Productivity...